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Depends on the scope, doesn't it? If our belief set consists of 1 belief like "all traffic offenders should be given a ticket" you might hit 90%. If it consists of a looser meta belief like "treat people kindly" that stands in for a dozen conflicting behavior norms, it gets messy
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Low-paradigm fields like modern anthropology have a particularist bias and suspicion of even empiricist triage theorizing of the "90%" type, let alone grand conceptual theories. While naive economists often have unreasonably consistent belief in market efficiency.
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A good way to consider ergodicity potential here is to simply think of case space as a different kind of time. You have a belief B on day t, based on case k. B(t,k) might need an update both for B(t+1,k) and for B(t, k+1).
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Is the situation more stable in time or case-space? It is not clear to me that different default assumptions for time/case-space are warranted. Your original heuristic amounts to "beliefs are more likely to be invalidated by new data over time than by new cases in scope"
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Yes, so it gets tricky, depending on how exploratory in case space the time behavior is, and what additional sources of invalidation there are besides case variety. So I do think time is probably a richer source of invalidations than case space, but it's not trivial to separate
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This is the tricky part where exploration speed is a variable. For example, a belief that "there is a 5 year cycle of recessions in the economy " will take 5 years to accrue ONE data point. But if you google historical data, you might invalidate it in 10 minutes. Near instant.
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This is roughly where the math lands (I'm coming to this question from control theory where there is a lot of work on exploration/exploitation tradeoffs in adaptive control where this question exists in a technical form). Horizons of adaptation are a key design variable.